Optimizing Meropenem Therapy for Severe Nosocomial Infections in Neonates.

Meropenem pharmacokinetics in neonates exhibits large interindividual variability due to developmental changes occurring during the first month of life. The objective was to characterize meropenem pharmacokinetics through a population approach to determine effective dosing recommendations in neonates with severe nosocomial infections. Three blood samples from forty neonates were obtained once steady-state blood levels were achieved and plasma concentrations were determined with a validated chromatographic method. Data were used to develop and validate the one-compartment with first-order elimination population pharmacokinetic model obtained by non-linear mixed effect modeling. The final model was Clearance (L/h) = 2.23 × Creatinine Clearance (L/h) and Volume of distribution(L) = 6.06 × Body Surface Area(m2) × (1 + 0.60 if Fluticasone comedication). Doses should be adjusted based on said covariates to increase the likelihood of achieving therapeutic targets. This model explains 12.9% of the interindividual variability for meropenem clearance and 19.1% for volume of distribution. Stochastic simulations to establish initial dosing regimens to maximize the time above the MIC showed that the mean probabilities to achieve the PK/PD target (PTA) for microorganisms with a MIC of 2 and 8 µg/mL were 0.8 and 0.7 following i.v. bolus of 250 and 500 mg/m2/dose q8h, respectively. Meropenem extended 4h infusion would improve PTA in neonates with augmented creatinine clearance.

Limited sampling strategies to predict the area under the concentration-time curve for rifampicin.

BACKGROUND: Rifampicin (RMP) is the most effective first-line antituberculosis drug. One of the most critical aspects of using it in fixed-drug combination formulations is to ensure it reaches therapeutic levels in blood. The determination of the area under the concentration-time curve (AUC) and appropriate dose adjustment of this drug may contribute to optimization of therapy. Even when the maximal concentration (Cmax) of RMP also predicts its sterilizing effect, the time to reach it (Tmax) takes 40 minutes to 6 hours. The aim of this study was to develop a limited sampling strategy (LSS) for therapeutic drug monitoring assistance for RMP. METHODS: Full concentration-time curves were obtained from 58 patients with tuberculosis (TB) after the oral administration of RMP in fixed-drug combination formulation. A validated high-performance liquid chromatographic method was used. Pharmacokinetic parameters were estimated with a noncompartmental model. Generalized linear models were obtained by forward steps, and bootstrapping was performed to develop LSS to predict AUC curve from time 0 to the last measured at 24 hours postdose (AUC0-24). The predictive performance of the proposed models was assessed using RMP profiles from 25 other TB patients by comparing predicted and observed AUC0-24. RESULTS: The mean AUC0-24 in the current study was 91.46 ± 36.7 mg·h·L, and the most convenient sampling time points to predict it were 2, 4 and 12 hours postdose (slope [m] = 0.955 ± 0.06; r = 0.92). The mean prediction error was -0.355%, and the root mean square error was 5.6% in the validation group. Alternate LSSs are proposed with 2 of these sampling time points, which also provide good predictions when the 3 most convenient are not feasible. CONCLUSIONS: The AUC0-24 for RMP in TB patients can be predicted with acceptable precision through a 2- or 3-point sampling strategy, despite wide interindividual variability. These LSSs could be applied in clinical practice to optimize anti-TB therapy based on therapeutic drug monitoring.